Systems and methods for a distributed data platform

ABSTRACT

A distributed data platform is able to predict whether data stored on a first network infrastructure component will be needed by a second network infrastructure component and prepares the data to be transmitted to the second network infrastructure component. The distributed data platform identifies a plurality of network infrastructure components connected to a network, and identifies data present on each component. The distributed data platform receives an indication that an event has occurred on the network and predicts whether the second network infrastructure component will require data stored by the first network infrastructure component. The distributed data platform prepares the data to be transmitted to the second network infrastructure component, and transmits the data after receiving an indication that the second network infrastructure component requires the data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit and priority to U.S. ProvisionalPat. Application Ser. No. 63/300,586, filed on Jan. 18, 2022 andentitled “SYSTEMS AND METHODS FOR A DISTRIBUTED DATA PLATFORM,” theentirety of which is hereby incorporated by reference.

In cases where the present application conflicts with a documentincorporated by reference, the present application controls.

BRIEF SUMMARY

Various components within a network infrastructure, such as towers, datacenters, data lakes, third party components, e.g. customer devicesoperating on the network, including the local storage and computingcapabilities of the customer devices, and other network infrastructurecomponents (collectively “network infrastructure components”) produceand consume large amounts of data. Typically, network infrastructurecomponents send and receive data from many other network infrastructurecomponents. In the case of a large network, such as a nation-widenetwork, this may cause delays in data being transferred, as it must betransferred across the country. Additionally, in the case of a largenetwork infrastructure, such as a nation-wide 5G network, the volume ofthe data produced may be larger than the total throughput of thenetwork. Thus, normal storage techniques, such as data warehouses, datalakes, and other data storage techniques, are unable to adequatelyhandle the large amount of data which must be stored. Furthermore, theproduction and consumption of data, along with transferring data acrossmultiple components may cause unnecessary duplicate data to be created.

The embodiments disclosed herein address the issues above and thus helpsolve the above technical problems and improve the technology of networkinfrastructure by providing a technical solution that provides adistributed data platform used to manage data storage and data movementthroughout the network. Additionally, the embodiments disclosed hereininclude guiding principles for managing data within the distributed dataplatform. Furthermore, the embodiments disclosed herein are able to beused to predict whether data will be required by a networkinfrastructure component, and to use that prediction to prepare the datato be transferred to the network infrastructure component which willrequire the data.

In some embodiments, a distributed data platform electronicallyidentifies network infrastructure components connected to a network,identifies data present on each network infrastructure component,receives an indication that an event has occurred on the network,predicts that a portion of data stored on a first network component willbe required by a second network infrastructure component, prepares theportion of data to be transmitted, receives an indication that thesecond network infrastructure component requires the portion of data,and causes the portion of data to be transmitted to the second networkinfrastructure component. In some embodiments, a network infrastructurecomponent transmits data indicating the data present on the networkinfrastructure component to a distributed data platform system, receivesan indication that a portion of the data stored by the networkinfrastructure component is to be transmitted to a second networkinfrastructure component, prepares the portion of the data to betransmitted, receives an indication that the second networkinfrastructure component requires the portion of the data, and transmitsthe portion of the data to the second network infrastructure component.In some embodiments, a network infrastructure data structure includesinformation specifying a plurality of network infrastructure components,information indicating data stored by each network infrastructurecomponent, and information indicating that an event has occurred on thenetwork, and is used to predict whether a network infrastructurecomponent will require certain data present on a network infrastructurecomponent.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 a is a display diagram showing an example network in which thedistributed data platform operates, according to various embodimentsdescribed herein.

FIG. 1 b is a display diagram showing an example OSS/BSS, according tovarious embodiments described herein.

FIG. 1 c is a display diagram showing an example message broker,according to various embodiments described herein.

FIG. 1 d is a display diagram showing an example NDC, according tovarious embodiments described herein.

FIG. 1 e is a display diagram showing an example RDC, according tovarious embodiments described herein.

FIG. 2 is a block diagram depicting example components incorporated in adistributed data platform system, according to various embodimentsdescribed herein.

FIG. 3 is a table diagram representing an infrastructure component datatable 300, according to various embodiments described herein.

FIG. 4 is a flow diagram depicting a process for predicting whether anetwork infrastructure component will require certain data.

FIG. 5 is a flow diagram depicting a process for operating a networkinfrastructure component in a distributed data platform, according tovarious embodiments described herein.

FIG. 6 is a flow diagram depicting a process for using an intermediarynetwork infrastructure component to prepare data to be transmittedbetween network infrastructure components, according to variousembodiments described herein.

FIG. 7 is a display diagram showing a use-case example of a sampletelecom network, according to various embodiments described herein.

DETAILED DESCRIPTION

Devices, network infrastructure, such as towers, data centers, etc., andother parts of a network’s infrastructure (“network infrastructurecomponents”) produce and consume large amounts of data in order toensure that a network, such as a telephone network, 5G/4G networks, andother networks which can span regions, nations, internationally, etc.The data produced and consumed by network infrastructure components istypically stored for use by the network infrastructure componentproducing the data, or for other network infrastructure components whichmay consume the data. Currently, the data produced by these componentsis stored in large warehouses or data lakes. However, transmitting datafrom one infrastructure component to another, or from a data lake ordata warehouse to a network infrastructure component, can cause delaysin data transmission, due to the distance that the data must travel.Furthermore, the amount of data used to manage and perform the normalfunctions of such a large network is larger than the total throughput ofthe network itself.

A common remedy is to replicate the data, such that it is stored inmultiple data lakes or warehouses, in order to lessen the distancetraveled by the data. However, this solution causes further issuesbecause each copy of the data must be updated when data is updated,which requires expending additional computing resources and computingpower. Additionally, the amount of data is increased with each piece ofdata duplicated, which further exacerbates bandwidth issues, as well asother issues, caused by the data being larger than the throughput of thenetwork. Thus, simply replicating the data, such as by having multipledata lakes or data warehouses to store copies of the data, is aninadequate solution for large networks, such as entire regional,nationwide, or international networks, with large amounts of networkinfrastructure components.

The embodiments disclosed herein address the issues above and help solvethe above technical problems and improve the technology of networkinfrastructure and data management for large networks by providing atechnical solution that provides a distributed data platform used tomanage data storage and data movement throughout the network.Additionally, the distributed data platform, or distributed dataplatform system, disclosed herein include guiding principles formanaging data within the distributed data platform. A distributed dataplatform is further able to predict which network infrastructurecomponents will require which data when certain events occur on thenetwork, and to use this prediction to prepare data for transmission tothe network infrastructure component, as well as to transmit the data tothe network infrastructure component when necessary. The distributednature of the distributed data platform further allows networkinfrastructure components to be more easily swapped out as newer, orupgraded, versions become available, than current methods of managinglarge amounts of data for a network.

In some embodiments, the distributed data platform identifies aplurality of network infrastructure components which are connected to anetwork. The distributed data platform may identify data present on eachnetwork infrastructure component. The distributed data platform mayreceive an indication that an event has occurred on the network, such asan error, an alert, a request by another network infrastructurecomponent or an end user, or any other event which may occur on anetwork. The distributed data platform may predict that data stored on afirst network infrastructure component will be requested by a secondnetwork infrastructure component based on the indication that the eventhas occurred. The distributed data platform may prepare the data to betransmitted to the second network infrastructure component. Thedistributed data platform may receive an indication that the secondnetwork infrastructure component requires the data. The distributed dataplatform may cause the data to be transmitted to the second networkinfrastructure component in response receiving the indication that thesecond network infrastructure component requires the data.

In some embodiments, the data is transmitted between the networkinfrastructure components in near real-time. The distributed dataplatform may utilize a message broker (“event streaming data source” or“broker”), such as a Kafka message broker, to transmit the data, or tocause the data to be transmitted. The distributed data platform maycause the data to be streamed from one network infrastructure componentto another. An example of an event streaming data source is that whichis provided by the Apache Kafka® event streaming platform. Such eventstreaming platforms may combine capabilities and functionality topublish (write) and subscribe to (read) streams of events, includingcontinuous import/export of data from other systems; store streams ofevents durably and reliably; and process streams of events as they occuror retrospectively.

In some embodiments, the data transmitted between the networkinfrastructure components is a model, such as a data model, simulationmodel, AI or machine learning model, analytics model, application,search, application programming interface (API), or another type ofalgorithm or code which operates on the data used by networkinfrastructure. Models may perform one or more operations on the data,such as scans, searches, processes, reductions, transformations, orother operations which may be performed on data. The model may be usedto process data accessible to the network infrastructure componentreceiving the data. A network infrastructure component may alter anaspect of the model as part of using the model. In some embodiments,after a network infrastructure component updates the model, the networkinfrastructure component indicates to other network infrastructurecomponents that the model has been altered. The network infrastructurecomponent which altered the model may cause the altered model to betransmitted to other network infrastructure components. The networkinfrastructure component may cause an indication of the alteration madeto the model to be transmitted to other network infrastructurecomponents. The alteration may include altered weights for the model,such as altered weights for a statistical model, altered weights for amachine learning model, etc. In some embodiments, in order to supportthe transmission of a model between network infrastructure components,each data pool for the respective network infrastructure component hasenough memory to store data relevant for the model’s operation and thecapability to apply the data to the model.

In some embodiments, preparing the data to be transmitted to anothernetwork infrastructure component includes transmitting the data to anintervening network infrastructure component. The intervening networkinfrastructure component may be geographically closer to the finaldestination of the data than the network infrastructure component whichoriginally produced the data. Thus, the data is able to be transmittedto its final destination in a shorter period of time when it is requiredby the network infrastructure component at the final destination.

In some embodiments, the distributed data platform includes one or moredata standards, or data policies, which each network infrastructurecomponent must follow when creating or receiving data. The distributeddata platform may examiner the data produced by a network infrastructurecomponent to determine whether the component is producing data accordingto the standards. The distributed data platform may transmit a message,alert, or other indication, to a network operator when the networkinfrastructure component is not producing data according to one or moredata standards which are based on one or more guiding principles for thedistributed data platform, or one or more guiding principles for thedata included in the distributed data platform. The guiding principlesfor the distributed data platform may be based on one or more keyprinciples, such as: ensuring the data platform is platform andarchitecture agnostic; ensuring the data platform is modular andconfiguration driven (such as through containerization), dynamic, oruses adaptive provisioning; ensuring interoperability through vendorsand network components; ensuring components are available forself-service; ensuring data management, movement, processing, etc., isbusiness value driven; ensuring the data platform supportsmulti-tenancy; ensuring the data governance is controlled by the entitycontrolling the network; ensuring the data quality throughout theplatform; ensuring the data platform has operational resilience andexcellence; ensuring the data platform includes automation, such asself-healing, ztp, and other automation used to manage a network;utilizing data operations and model operations; and other principlesused to guide the configuration of a data platform. The guidingprinciples for the data may be based on one or more key principles, suchas: the network provider owning all of the data; democratizing the data;defining data agreements; using a data lake for support; storing dataschema-free; using an ecosystem of data products; avoiding vendorlock-in data designs; self-documenting data models and products;utilizing domain driven data products; and other principles used toguide the configuration of data standards.

In some embodiments, a network infrastructure component transmits aportion of the data it has stored to one or more other networkinfrastructure components. The network infrastructure component may becontrolled by an entity other than the entity which controls the network(referred to as a “third party”). In some embodiments, the networkinfrastructure component uses a message broker, such as a Kafka messagebroker, to transmit the portion of the data. In some embodiments, thenetwork infrastructure component streams the portion of the data. Insome embodiments, the portion of the data includes a model. In someembodiments, the network infrastructure component receives an indicationthat the model was altered, and alters the model based on the indicationthat the model was altered. In some embodiments, preparing the portionof the data to be transmitted includes receiving an indication of anintermediary network infrastructure component and transmitting the datato the intermediary network infrastructure component.

In some embodiments, the distributed data platform utilizes a networkinfrastructure component data structure, which includes informationdescribing each network infrastructure component in the distributed dataplatform, to perform the functions described herein. The networkinfrastructure component data structure may include informationdescribing the data stored by each network infrastructure component. Thedata stored by a network infrastructure component may include a model.The network infrastructure component data structure may include alocation, such as a geographic location, a relative location, etc., ofone or more network infrastructure components. The networkinfrastructure component data structure may include data describing therelationship between one or more network infrastructure components. Thenetwork infrastructure component data structure may include informationindicating a network infrastructure component which is used to preparedata transmitted from one network infrastructure component to another.The network infrastructure component data structure may includeinformation specifying one or more data standards regarding how data isstored, produced, consumed, or managed, or regarding some combination ofhow the data is stored, produced, consumed, or managed.

In some embodiments the distributed data platform includes a centralizedmanagement function. The centralized management function may be used toarchitect how information is accessed, how frequently information isupdated, the life cycle of models, etc. The centralized managementfunction may determine how data is consumed, how models are executed,how network infrastructure components behave when receiving certain dataor models, etc. The centralized management function may performscheduling in order to manage data between network infrastructurecomponents. The centralized management function may control one or moreof: access to data and models, policies for data and models, auditing ofdata and models, orchestration of requests for data and models, etc.

In some embodiments, the distributed data platform includes an accesscontrol function. The access control function may be implemented as partof a centralized management function. The access control may beimplemented by using accounts, access management rules, applicationtrigger tags, or other mechanisms to control access to data. The accesscontrol function may be implemented as a centralized system. The accesscontrol function may be implemented by using a network of access systemswhich are synchronized. The distributed data platform may control accessto the data based on the type of data requested. The distributed dataplatform may control access to data by encrypting the data. In someembodiments, some data which is related may be stored in multipleseparate physical network infrastructure components in order to increasesecurity, and are only allowed to be accessed together when a networkinfrastructure component has access to all of the related data. Forexample, a two sets of data may be interrelated, but stored on twoseparate network infrastructure components. A third networkinfrastructure component would need access to both of the networkinfrastructure components storing the data in order to access the twosets of data at the same time.

In some embodiments, the distributed data platform includes acentralized catalog and model. The centralized catalog and model may beimplemented as part of a centralized management function. Thecentralized catalog and model may be used by the distributed dataplatform to understand data obtained from network infrastructurecomponents. The centralized catalog and model may be used to facilitatethe orchestration of requests, such as a request for data, a request fora model, a request for specified data to be applied to a model, etc. Thecentralized catalog and model may be used by the centralized managementfunction to determine where and how to fulfill a request.

In some embodiments, the distributed data platform registers a new datasource. A new data source may be registered when a networkinfrastructure component is connected to the network. The distributeddata platform may ensure that data and models produced, consumed, usedby, generated, etc., by the data source follows the policies, rules,etc., used by the distributed data platform. The distributed dataplatform may ensure that requests for data or models by the new datasource are made in accordance with the policies, rules, etc., used bythe distributed data platform. The distributed data platform may ensurethat data obtained from the new data source is stored in a certainformat. In some embodiments, the format is used by all of the networkinfrastructure components which are included in the distributed dataplatform, such that the data does not need to be converted to anotherformat for a network infrastructure component to use the data.

In some embodiments, when a network infrastructure component makes a newrequest for data or a model, the distributed data platform performs thedata operations, data processing, data movement, etc., required toprovide the data or model to the network infrastructure component. Thedistributed data platform may ensure that the request, the requesteddata or model, the transmission of the requested data or model, etc.,conform to the policies, rules, etc., used by the distributed dataplatform. For example, the distributed data platform may ensure that thenetwork infrastructure component requesting the data or model has accessto the data or model in the first place, including ensuring that theproper credentials are acquired to grant access. The distributed dataplatform may ensure that the requested data or model is transmitted in acertain format.

In some embodiments, the distributed data platform uses one or more of:cataloging, metadata logging, policies, etc., to control the use of,access to, consumption of, and process of obtaining data.

Unless the context requires otherwise, throughout the specification andclaims which follow, the word “comprise” and variations thereof, suchas, “comprises” and “comprising” are to be construed in an open,inclusive sense, for example “including, but not limited to.”

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. Thus, the appearances of the phrases “in one embodiment” or“in an embodiment” in various places throughout this specification arenot necessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined inany suitable manner in one or more embodiments.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural referents unless the contentclearly dictates otherwise. The term “or” is generally employed in itssense including “and/or” unless the content clearly dictates otherwise.

The headings and Abstract of the Disclosure provided herein are forconvenience only and do not interpret the scope or meaning of theembodiments.

FIG. 1 a is a display diagram showing an example network 100 in whichthe distributed data platform operates, according to various embodimentsdescribed herein. It is to be appreciated that FIG. 1 a illustrates justone example of a network in which a distributed data platform operates,and that various embodiments discussed herein are not limited to the useof such an environment. The network 100 includes consumer models 101, aNetwork Switching Subsystem (NSS) 103, an operations support system andbusiness support system (OSS/BSS) 105, a message broker 107, and a datalake 113. The NSS 103 includes a national data center (NDC) 109 aregional data center (RDC) 111. The data lake 113 includes a data lakeorchestrator 193, which includes a producer 195 and a consumer 197.

The network 100 is a network, communication system, or networked system(not shown), NSS 103, OSS/BSS 105, message broker 107, date lake 113,NDC 109, RDC 111, and network infrastructure devices (not shown), andother network functions (not shown), may be coupled, and on which theconsumer models 101 operate. Non-limiting examples of such a network orcommunication system include, but are not limited to, an Ethernetsystem, twisted pair Ethernet system, an intranet, a local area network(LAN) system, short range wireless network (e.g., Bluetooth®), apersonal area network (e.g., a Zigbee network based on the IEEE 802.15.4specification), a Consumer Electronics Control (CEC) communicationsystem, Wi-Fi, satellite communication systems and networks, cellularnetworks, cable networks, or the like. One or more endpoint devices,such as PCs, tablets, laptop computers, smartphones, personalassistants, Internet connection devices, wireless LAN, WiFi, WorldwideInteroperability for Microwave Access (WiMax) devices, or the like, maybe communicatively coupled to the network and/or to each other so thatthe plurality of endpoint devices are communicatively coupled together.Thus, such a network enables the consumer models 101, NSS 103, OSS/BSS105, message broker 107, date lake 113, NDC 109, RDC 111, other networkinfrastructure components, and other network functions, used as part ofthe distributed data platform, to communicate with each other.

The consumer models 101 may include artificial intelligence, machinelearning models, analytics models, data models, applications, and othermodels. The consumer models 101 may be stored on, used by, accessed,etc., network infrastructure components. The consumer models 101 mayalso communicate with, access, use, etc., other consumer models 101. Theconsumer models 101 are used by the network 100 to perform at least someof the functions necessary to operate the network 100.

FIG. 1 b is a display diagram showing an example OSS/BSS 105, accordingto various embodiments described herein. The OSS/BSS 105 includes aconnection to a third party component, such as an inventory database121, or other databases for defined purposes, for example maintaining aninventory of network elements, applications, and topology, andconnections to other third party vendors such as the vendor 126. Theinventory database 121 includes a producer 123 and consumer 125. Theproducer 123 represents data produced by the inventory database 121 andthe consumer 125 represents data consumed by the inventory database 121.The vendor 126 includes a producer 127, a consumer 129, and a vendorpool 131. The producer 127 and consumer 129 are similar to the producer123 and consumer 125, respectively. The vendor pool 131 represents apool of data stored by the vendor 126. The various vendor pools, datapools, and data lakes, which appear in FIGS. 1 a - 1 e , may belogically separated datasets within a region, or based on location.While FIG. 1 b includes an OSS/BSS 105, one of skill in the art wouldrecognize that an NSS, OSS, BSS, or any combination of the three, couldbe used by the distributed data platform.

FIG. 1 c is a display diagram showing an example message broker 107,according to various embodiments described herein. The message broker107 is used to facilitate communication between network infrastructurecomponents. In some embodiments, the message broker 107 enables networkinfrastructure components to stream data to each other. In someembodiments, the role of the message broker 107 is performed by a Kafkamessage broker. The message broker 107 includes rules 133, components137, a producer 139, and a consumer 141. The rules 133 include rules fortransmitting and receiving data from one network infrastructurecomponent to another. The components 137 include components of themessage broker 107 which are used to facilitate communication betweennetwork infrastructure components. The producer 139 and consumer 141operate in a similar manner to the producer 123 and consumer 125,respectively.

FIG. 1 d is a display diagram showing an example NDC 109, according tovarious embodiments described herein. The NDC 109 includes anorchestrator 149, a network function (xNF) 163, an NDC vendor 155, and adata pool 169. The orchestrator 149, xNF 163, and NDC vendor 155 eachinclude a producer and consumer which operates in a similar manner tothe producer 123 and consumer 125, respectively. The NDC vendor 155includes a vendor pool 161 which operates in a similar manner to thevendor pool 131. The data pool 169 stores data used by, produced by, oris otherwise stored by, the NDC 109.

FIG. 1 e is a display diagram showing an example RDC 111, according tovarious embodiments described herein. The RDC 111 includes an onboardfunction (OBF) 171, other functions 179, an xNF 185, and a data pool191. The OBF 171, other functions 179, and xNF 185, each include aproducer and consumer which operate in a similar manner to the producer123 and consumer 125, respectively. The OBF 171 further includes avendor pool 177 which is similar to the vendor pool 131. The data pool191 operates in a similar manner to the data pool 169.

While FIGS. 1 a-1 e depict the several components as part of adistributed data platform, one of skill in the art would recognize thatthese components may be layered and exist across many different physicallocations, such as a national data center, regional data center, otherdata centers, cell sites, etc. Furthermore, one of skill in the artwould recognize that each of the components described in connection withFIGS. 1 a-1 e may produce data or be monitored by specific components toproduce data. Furthermore, one of skill in art would recognize that datapools, which may together comprise a distributed data lake, arepotentially present in each physical, logical, or virtual instance.

Additionally, one of skill in the art would recognize that the OSS/BSSand NDC/RDC are examples and may not be relevant in all embodiments orimplementations of the distributed data platform. Such concepts may alsobe extensible to other network structures and components, such as MEC,LZ/Outposts (“EDC”), etc.

In some embodiments, one or more of the components of the distributeddata platform, including the components described in connection withFIGS. 1 a-1 e , may include databases, such as: persistent databasesserving a known set of functions as an aggregation point for a subset ofdata, for example, a data inventory; transient on-demand working datasets; or other databases or data sets used to perform the operations ofthe components, the distributed data platform, etc.

Example embodiments described herein provide applications, tools, datastructures and other support to implement systems and methods foroperating a distributed data platform. The example embodiments describedherein additionally provide applications, tools, data structures andother support to implement systems and methods for moving datathroughout the distributed data platform. Other embodiments of thedescribed techniques may be used for other purposes, including fordetermine whether an event has occurred, and whether certain data may berequested by a network infrastructure component. In the descriptionprovided herein, numerous specific details are set forth in order toprovide a thorough understanding of the described techniques. Theembodiments described also can be practiced without some of the specificdetails described herein, or with other specific details, such aschanges with respect to the ordering of processes or devices, differentprocesses or devices, and the like. Thus, the scope of the techniquesand/or functions described are not limited by the particular order,selection, or decomposition of steps described with reference to anyparticular module, component, or routine.

FIG. 2 is a block diagram depicting example components incorporated in adistributed data platform system, according to various embodimentsdescribed herein. The distributed data platform system 200 may be:located on the network 100 in a position to communicate with a networkinfrastructure component; integrated as part of a network infrastructurecomponent; integrated on a as part of a plurality of networkinfrastructure component; or portions of the distributed data platformmay be integrated in a plurality of network infrastructure components.In various embodiments, the distributed data platform system 200includes one or more of the following: a computer memory 201 for storingprograms and data while they are being used, including data associatedwith the various network infrastructure components and the distributeddata platform system 200, an operating system including a kernel, anddevice drivers; a central processing unit (CPU) 202 for executingcomputer programs; a persistent storage device 203, such as a hard driveor flash drive for persistently storing programs and data; and a networkconnection 204 for connecting to one or more network infrastructurecomponents and/or other computer systems, to send and/or receive data,such as via the Internet or another network and associated networkinghardware, such as switches, routers, repeaters, electrical cables andoptical fibers, light emitters and receivers, radio transmitters andreceivers, and the like, and to scan for and retrieve signals fromnetwork infrastructure components, and/or other network functions, andfor connecting to one or more computer devices such as networkinfrastructure components and/or other computer systems. In variousembodiments, the CPU 202 may be a GPU, a DPU, or other “compute”hardware capable of performing logical operations. In variousembodiments, the persistent storage device 203 may be, or include, atemporary storage device. In various embodiments, the distributed dataplatform system 200 additionally includes input and output devices, suchas a keyboard, a mouse, display devices, etc.

While a distributed data platform system 200 configured as described maybe used in some embodiments, in various other embodiments, thedistributed data platform system 200 may be implemented using devices ofvarious types and configurations, and having various components. Thememory 201 may include a distributed data platform controller 210 whichcontains computer-executable instructions that, when executed by the CPU202, cause the distributed data platform system 200 to perform theoperations and functions described herein. For example, the programsreferenced above, which may be stored in computer memory 201, mayinclude or be comprised of such computer-executable instructions. Thememory 201 may also include a network infrastructure component datastructure.

The distributed data platform controller 210 performs the core functionsof the distributed data platform system 200, as discussed herein andalso with respect to FIGS. 3 through 7 . In particular, the distributeddata platform controller 210 facilitates the management of dataproduced, consumed, stored, or otherwise used or accessible by thedistributed data platform. Additionally, the distributed data platformcontroller 210 may determine whether data may be required by a networkinfrastructure component when an event occurs. The distributed dataplatform controller 210 may also determine prepare the data to betransferred to the other network infrastructure component. In someembodiments, the distributed data platform controller 210 uses a messagebroker. In some embodiments, the distributed data platform controller210 uses statistical analysis, an artificial intelligence or machinelearning model trained to predict whether certain data will be used by anetwork infrastructure component when an event occurs, or somecombination to determine whether data will be requested by a secondnetwork infrastructure component. In some embodiments, the distributeddata platform controller 210 includes models which can be transmitted toother network infrastructure components. In some embodiments, thedistributed data platform controller 210 includes one or more consumermodels used to process, manage, or otherwise consume data produced by anetwork infrastructure component, or received from another networkinfrastructure component.

In an example embodiment, the distributed data platform controller 210and/or computer-executable instructions stored on memory 201 of thedistributed data platform system 200 are implemented using standardprogramming techniques. For example, the distributed data platformcontroller 210 and/or computer-executable instructions stored on memory201 of the distributed data platform system 200 may be implemented as a“native” executable running on CPU 202, along with one or more static ordynamic libraries. In other embodiments, the distributed data platformcontroller 210 and/or computer-executable instructions stored on memory201 of the distributed data platform system 200 may be implemented asinstructions processed by a virtual machine that executes as some otherprogram.

The embodiments described above may also use synchronous or asynchronousclient-server computing techniques. However, the various components maybe implemented using more monolithic programming techniques as well, forexample, as an executable running on a single CPU computer system, oralternatively decomposed using a variety of structuring techniques knownin the art, including but not limited to, multiprogramming,multithreading, client-server, or peer-to-peer, running on one or morecomputer systems each having one or more CPUs. Some embodiments mayexecute concurrently and asynchronously, and communicate using messagepassing techniques. Equivalent synchronous embodiments are alsosupported. Also, other functions could be implemented and/or performedby each component/module, and in different orders, and by differentcomponents/modules, yet still achieve the functions of the distributeddata platform system 200.

In addition, programming interfaces to the data stored as part of thedistributed data platform controller 210 can be available by standardmechanisms such as through C, C++, C#, Java, and web APIs; libraries foraccessing files, databases, or other data repositories; throughscripting languages such as JavaScript and VBScript; or through Webservers, FTP servers, or other types of servers providing access tostored data. The distributed data platform controller 210 may beimplemented by using one or more database systems, file systems, or anyother technique for storing such information, or any combination of theabove, including implementations using distributed computing techniques.

Different configurations and locations of programs and data arecontemplated for use with techniques described herein. A variety ofdistributed computing techniques are appropriate for implementing thecomponents of the embodiments in a distributed manner including but notlimited to TCP/IP sockets, RPC, RMI, HTTP, Web Services (XML-RPC,JAX-RPC, SOAP, and the like). Other variations are possible. Also, otherfunctionality could be provided by each component/module, or existingfunctionality could be distributed amongst the components/modules indifferent ways, yet still achieve the functions of the distributed dataplatform system 200 and network infrastructure components.

Furthermore, in some embodiments, some or all of the components/portionsof the distributed data platform controller 210, and/or functionalityprovided by the computer-executable instructions stored on memory 201 ofthe distributed data platform system 200 may be implemented or providedin other manners, such as at least partially in firmware and/orhardware, including, but not limited to, one or moreapplication-specific integrated circuits (ASICs), standard integratedcircuits, controllers (e.g., by executing appropriate instructions, andincluding microcontrollers and/or embedded controllers),field-programmable gate arrays (FPGAs), complex programmable logicdevices (CPLDs), and the like. Some or all of the system componentsand/or data structures may also be stored as contents (e.g., asexecutable or other machine-readable software instructions or structureddata) on a computer-readable medium (e.g., as a hard disk; a memory; acomputer network or cellular wireless network; or a portable mediaarticle to be read by an appropriate drive or via an appropriateconnection, such as a DVD or flash memory device) so as to enable orconfigure the computer-readable medium and/or one or more associatedcomputing systems or devices to execute or otherwise use or provide thecontents to perform at least some of the described techniques. Suchcomputer program products may also take other forms in otherembodiments. Accordingly, embodiments of this disclosure may bepracticed with other computer system configurations.

In general, a range of programming languages may be employed forimplementing any of the functionality of the servers, functions, userequipment, etc., present in the example embodiments, includingrepresentative implementations of various programming language paradigmsand platforms, including but not limited to, object-oriented (e.g.,Java, C++, C#, Visual Basic.NET, Smalltalk, and the like), functional(e.g., ML, Lisp, Scheme, and the like), procedural (e.g., C, Pascal,Ada, Modula, and the like), scripting (e.g., Perl, Ruby, PHP, Python,JavaScript, VBScript, and the like) and declarative (e.g., SQL, Prolog,and the like).

FIG. 3 is a table diagram representing an infrastructure component datatable 300, according to various embodiments described herein. Theinfrastructure component data table 300 includes a component id column310, a data produced by component column 311, and a data consumed bycomponent column 312. In some embodiments, the infrastructure componentdata table 300 includes additional columns used to identify accesscontrol, security, etc. for components identified in the infrastructurecomponent data table. The component id column 310 includes informationused to specify a specific network infrastructure component. The dataproduced by component column 311 includes information used to specifydata that a component produces. The data consumed by component 312includes data that a component consumes.

For example, as shown in row 301, the NDC Orchestrator may produceinventory data requested by a network infrastructure component, amongother types of data. Further, the NDC Orchestrator may consume inventorydata received from an inventory database and data received from anOnboard Function, or OBF, among other types of data. In someembodiments, the inventory data includes: virtual, physical, or logicalinstance data; location data; change data/metadata; and relationshipsbetween the inventory data. As another example, as shown in row 302, theOSS Inventory produces inventory data and consumes element state data,requested inventory data, and physical, expected, or planned metadata.

As yet another example, the RDC in row 303 does not produce data, as theRDC performs functions, but the RDC OBF of row 304 produces datadescribing the RDC. The RDC consumes inventory data requested by anetwork infrastructure component. The RDC OBF of row 304 produces dataregarding faults, alerts, performance metrics, and other onboardfunction data. The RDC OBF consumes inventory data, such as the dataproduced by the OSS Inventory of row 302. In this example, and in someembodiments, the RDC onboard function is present in more parts of thenetwork than the RDC itself.

FIG. 4 is a flow diagram depicting a process for predicting whether anetwork infrastructure component will require certain data. At act 401,the distributed data platform identifies network infrastructurecomponents connected to a network. At act 403, the distributed dataplatform identifies data present on each network infrastructurecomponent. At act 405, the distributed data platform receives anindication that an event has occurred on the network.

At act 407, the distributed data platform predicts that data stored, orpresent, on a first network infrastructure component will be requestedby a second network infrastructure component. In some embodiments, thedistributed data platform predicts whether the data will be requested byusing one or more of: statistical analysis, artificial intelligence, ora machine learning model trained to predict whether data will berequired by a network infrastructure component based on at least anindication that an event has occurred.

At act 409, the distributed data platform prepares the data to betransmitted from the first network infrastructure component to thesecond network infrastructure component. In some embodiments, preparingthe data for transmission includes transmitting the data to a thirdnetwork infrastructure component.

At act 411, the distributed data platform receives an indication thatthe second network infrastructure component requires the data. At act413, the distributed data platform causes the data to be transmitted tothe second network infrastructure component. In some embodiments thedata is transmitted to the second infrastructure component in real time.The data may be transmitted using a message broker, such as a Kafkamessage broker. The data may be streamed to the second networkinfrastructure component. After act 413, the process ends.

FIG. 5 is a flow diagram depicting a process for operating a networkinfrastructure component in a distributed data platform, according tovarious embodiments described herein. At act 501, the networkinfrastructure component transmits data indicating the data stored bythe network infrastructure component to a distributed data platformsystem. At act 503, the network infrastructure component receives anindication that a portion of the data stored by the networkinfrastructure component is to be prepared to be transmitted to anothernetwork infrastructure component.

At act 505, the network infrastructure component prepares the portion ofthe data to be transmitted to the other network infrastructurecomponent. At act 507, the network infrastructure component receives anindication that the other network infrastructure component requires theportion of the data. At act 509, the network infrastructure componenttransmits the portion of the data. After act 509, the process ends.

FIG. 6 is a flow diagram depicting a process for using an intermediarynetwork infrastructure component to prepare data to be transmittedbetween network infrastructure components, according to variousembodiments described herein. At act 601, a distributed data platformreceives an indication that a second network infrastructure componentrequires data stored on a first network infrastructure component. At act603, the distributed data platform causes the data on the first networkinfrastructure component to be prepared to be transmitted to the secondnetwork infrastructure component.

At act 605, the distributed data platform causes the data to betransmitted from the first network infrastructure component to a thirdnetwork infrastructure component. In some embodiments, the third networkinfrastructure component is identified based on a logical relation,geographic location, or another method of identifying a properintermediary network infrastructure component.

At act 607, the distributed data platform receives an indication thatthe second network infrastructure component requires the data. At act609, the distributed data platform causes the data to be transmittedfrom the third network infrastructure component to the second networkinfrastructure component. After act 609, the process ends.

FIG. 7 is a display diagram showing a use-case example of a sampletelecom network 700, according to various embodiments described herein.The telecom network 700 includes infrastructure components, such as theconsumer models 701, an NSS 703, an OSS/BSS 705, a message broker 707,an NDC 709, an RDC 711, and a data lake 713. The consumer models 701 aresimilar to the consumer models 101. The NSS 703 is similar to the NSS103. The OSS/BSS 705 is similar to the OSS/BSS 105. The message broker707 is similar to the message broker 107. The NDC 709 is similar to theNDC 109. The RDC 711 is similar to the RDC 111. The data lake 713 issimilar to the data lake 113. Each of the components in FIG. 7 includesub-components which are the same or similar to those correspondingsimilar components in FIGS. 1A to 1E. In some embodiments, the networkinfrastructure components use transit gateways to transmit data outsideof the respective network infrastructure component.

In the sample telecom network 700, one or more consumer models 701 existon each of the network infrastructure components shown in the telecomnetwork. The consumer models 701 are used by the telecom network toperform the at least part of the functions provided by the telecomnetwork.

The sample telecom network 700 may additionally include layers, such asthe data virtualization layer 715, the data governance layer 717, andthe data infrastructure as a service layer 719. The data virtualizationlayer 715 is a semantic layer of data that makes data across the networkavailable to network infrastructure components. The data virtualizationlayer 715 is thus able to logically keep the data together. The datavirtualization layer 715 may include data definitions describing whatdata exists, where the data exists, the types of data structures, etc.

The data governance layer 717 is a layer which manages the securitypermissions, rules, polices, regulations, and other aspects which allowdata governance. This may include, but is not limited to: how data isdistributed; the impact of certain roles, of network infrastructurecomponents, users, and others trying to access the data, on access todata; the ownership of the data; and other aspects related to how datais governed.

The data infrastructure as a service layer (or “data infrastructurelayer”) 719 is a layer which manages the diverse architectures andtechnologies used by the telecom network 700. The data infrastructurelayer 719 is able to make the infrastructure of the telecom network,such as the network infrastructure components, the models used bynetwork infrastructure components, the data produced by networkinfrastructure components, etc., available to third parties, as well asother network infrastructure components.

The example telecom network 700 may be developed with a variety ofguiding principles for the data. The following principles are guidingprinciples used in the example telecom network 700, however otherprinciples may be used. Furthermore, while a variety of principles aredescribed below, any number of the following principles, otherprinciples, etc., may be used in various embodiments. Under oneprinciple, the owner of the telecom network 700 owns all of the data,including data produced by third parties.

Under another principle, the data is democratized to support agileconsumer models, including business and network analytics. Data accessmay be enforced based on polices, such as security policies,non-disclosure agreements, customer agreements, and other policies whichaffect the use of data. Domain datasets are distributed, discoverable,and able to be accessed, controlled, and governed by networkinfrastructure components. Furthermore, all data in the data lake isgoverned to enable enrichment by data consumers and “reconciliation” bydata lake operations.

Another principle states that data agreements are defined with allsources and source types. This may include the data payload, ingestpatterns, location of data, intent of onboarding data, etc. Vendors mustconform to supported data formats and structures, protocols for dataingestion, and must have integration capabilities with tools defined bythe network owner.

According to another principle, the data lake should support the dataplatform. Supporting the platform may include enabling a variable datastructure, assisting with latency, assisting with the volume of data,assisting with the quality of service for users of the network, andassisting with pre-defined or on-demand needs for network infrastructurecomponents.

Another principle states that data should be stored schema-free. Thus,the data is modeled into a fixed schema as late as possible, such as,for example, right before use by a network infrastructure component.

Another principle states that the data platform provides an ecosystem ofconsumable data products, such as data warehousing, data services, orsemantic layers, as independent version-aware terminal points. The dataplatform also provides consumer models in consumable formats to avoidadditional programing for preparation. Furthermore, data computationtechniques, modelling, semantic layers, and data feedback is availableand independent of the data itself.

Another principle states that the vendors cannot “lock-in” data designs,logic, and dependencies. Thus, data is portable between differenthosting environments, frameworks, etc., in an agile manner.

Another principle states that data models and products are to beself-documented and propagated with access to all dependences. The datamodels and products should be accessible programmatically.

Another principle states that domain driven data products are madeavailable. This may include data, metadata, and semantic data being ableto adapt to changes in definition and meaning. Thus, the data is keptindependent of the network infrastructure technology and components.

The example telecom network 700 may be developed with a variety ofguiding principles for the data platform. The following principles areguiding principles used in the example telecom network 700, howeverother principles may be used. Under one principle, the owner of thetelecom network 700 up to one data pool may be used in a networkinfrastructure component. Under this principle, an additional data poolis not created if a vendor pool covers the entire dataset in adatacenter. Furthermore, one data lake is used by the network owner.

According to another principle, a data pool or data lake is to be placedcloser to the source of data and should support fit-for-purposearchitectures. The data pool or data lake can support hybridenvironments, such as on-prem, public cloud, or other environments fordistributed computing and storage architectures. Furthermore, dataprocurement processes should consider matching stipulations andcharacteristics to the network owner’s data lake or data pools forinteractions.

According to another principle, all components and capabilities are tobe built modular as microservices. The microservices are to have REST orother enabled interfaces, and should support dynamic configuration ofcomponents, like storage, computing, workflows, and frameworks. Themicroservices should also support capabilities such as, onboarding,instantiation, provisioning, resource scaling, etc. The The architectureshould be decoupled to allow for flexibility to add or remove componentsand capabilities as and where needed. Furthermore, the data platformshould have a unified orchestration, governance, and metadata paradigmenable architecture across distributed data pools and the data lake.Frameworks and processes within the data platform should be capable toadapt to newer formats and data structures over time.

Another principle states that the architecture should supportbi-directional interworking with external niche vendors and frameworksfor specialized capabilities, such as databricks, orchestration productsfor data, metadata, logic, etc., and vendor data pools.

Another principle states that the data lake capabilities and componentsmust be available for self-service including data access, exploration,source onboarding, pipeline designs, scaling, monitoring, performancecapabilities, etc. Additionally, the architecture should be able toserve as the backend to support data analytics.

Under another principle, capabilities like data-movement,data-processing, etc. at all data lake components are regulated by thenetwork owner’s cost-to-value evaluations. The evaluations are definableand enforceable to all capabilities, components and work-flow tiers.Furthermore, onboarding, ingesting, data movement and processing withina data lake should be driven by a business, consumer, operations,research, or automation use case. The data platform and networkinfrastructure components should implement and use existing technologiesprior to looking for new architectures, technology and tools.

According to another principle, network infrastructure components shouldbe able to securely isolate their capabilities, components, andwork-flows, including components under the control of third parties.

According to another principle, data configuration should be driven byactive and agile governance for data quality and data security(including de-identification, classification based routing, privilegemanagement) at all levels of data, metadata, processes, and work-flowsto support regulatory requirements, compliance requirements, or othercustomer requirements. The network owner should control all datagovernance policies, and the governance policies should extend to allcustomers and systems interworking with the network’s data.

Under another principle, the data platform should include proactive andreactive governance models with version management of the policies ateach tier.

Another principle states that the data lake should be highly availableand durable, with as little downtime as possible. The tiers of the datalake and their specific requirements may be defined to implement dynamicoptimization of services, operations, features, etc. The data lakecomponents and capabilities may be designed to adapt to bettertechnologies over time. The data platform should have a unifiedorchestration and centralized governance and discoverability for theplatform, the distributed data, and the models.

Another principle states that the “dev-ops,” continuous integration, andcontinuous deployment) should be used to automate incident response,performance and cost for reactive and proactive optimizations, codereleases, configuration and change management and data migration needs,while preventing data losses and protecting from threats. Furthermore,third parties are to support and manage the lifecycle of their products,partners, open source components, etc.

Another principle states that like new sandboxes, continuous metaorchestration, continuous testing, and continuous monitoring are used bythe network owner to manage the quality, cycle-time, or data analyticsfrom data in production. Furthermore, models, such as AI models, machinelearning models, and other consumer models should be used for dataprocessing and consumption.

In an example embodiment, and by utilizing at least some of theprocesses, principles, components, etc., described herein, thedistributed data platform is able to provide a data consumer or dataproducer (collectively a “vendor”), with access to data produced orconsumed by the network. In this example embodiment, the distributeddata platform is able to provide the vendor with dynamic access to dataobservability, data streaming, data lakes and data pools, inventorydatabases, a design studio for designing network applications, defectlogs and data enrichment, end to end digital twins, data patterns andtremors, golden clusters, testing for SLOs, as-built performance forapplications, etc.

In this example embodiment, the distributed data platform may includeprinciples, such as those described above. As an example, some of theprinciples may include: requiring all data to flow through the platformto prevent “orphan data” and disconnected systems; having a distributednetwork which includes an interconnected system of data lakes and poolswith dynamic, on-demand, edge-intelligent properties; building the dataplatform as an application running on the network which is built withcloud-native principles; allowing customers, vendors, and partners toconsume and contribute data; and using data to enhance the network.

Furthermore, in this example embodiment, the distributed data platformmay include multiple layers, such as: an agent layer for user-systemsperforming data operations; an enrichment layer for systems, includingdistributed processing systems, combining and processing data; a sourcelayer for systems, including distributed processing systems, storing andtransferring data; and a control plan for data management. The controlplane may enforce data policies, monitor data, observe data flow, andmanage distributed processing for the data produced at and flowingbetween each layer. The control plane ensures that data is cataloged andthat all data is accessed via centralized roles.

Thus, in this example embodiment, the distributed data platform is ableto facilitate the distribution of data across the entire network bystoring data locally in network infrastructure components and using thecontrol plane to allow components or devices at each layer to access thedata.

The various embodiments described above can be combined to providefurther embodiments. All of the U.S. patents, U.S. patent applicationpublications, U.S. patent applications, foreign patents, foreign patentapplications and non-patent publications referred to in thisspecification and/or listed in the Application Data Sheet areincorporated herein by reference, in their entirety. Aspects of theembodiments can be modified, if necessary to employ concepts of thevarious patents, applications and publications to provide yet furtherembodiments.

These and other changes can be made to the embodiments in light of theabove-detailed description. In general, in the following claims, theterms used should not be construed to limit the claims to the specificembodiments disclosed in the specification and the claims, but should beconstrued to include all possible embodiments along with the full scopeof equivalents to which such claims are entitled. Accordingly, theclaims are not limited by the disclosure.

1. A system comprising: at least one processor; and at least one memorycoupled to the at least one processor, the memory havingcomputer-executable instructions stored thereon that, when executed bythe at least one processor, cause the system to perform: electronicallyidentifying a plurality of network infrastructure components, eachnetwork infrastructure component being connected to a network; for eachnetwork infrastructure component of the plurality of networkinfrastructure components: electronically identifying data present onthe network infrastructure component; electronically preparing data tobe transmitted from a first network infrastructure component to a secondnetwork infrastructure component; electronically receiving an indicationthat the second network infrastructure component requires the prepareddata; and electronically causing the data to be transmitted to thesecond network infrastructure component.
 2. The system of claim 1,wherein electronically causing the prepared data to be transmitted tothe second network infrastructure component comprises: electronicallycausing the prepared data to be transmitted to the second networkinfrastructure component in near-real time.
 3. The system of claim 2,wherein electronically causing the prepared data to be transmitted tothe second network infrastructure component in near-real furthercomprises: electronically utilizing a message broker to cause the datato be transmitted.
 4. The system of claim 2, wherein electronicallycausing the prepared data to be transmitted to the second networkinfrastructure component further comprises: electronically causing theprepared data to be streamed to the second network infrastructurecomponent.
 5. The system of claim 1, wherein the prepared data furthercomprises: a model used by the second network infrastructure componentto process data accessible by the second network infrastructurecomponent.
 6. The system of claim 5, wherein the computer-executableinstructions further cause the system to: electronically receive anindication that the second network infrastructure component has alteredan aspect of the model; and electronically cause a third networkinfrastructure component which uses the model, to receive the alteredmodel.
 7. The system of claim 6, wherein the altered aspect of the modelcomprises weights of the model.
 8. The system of claim 1, whereinpreparing the data to be transmitted to the second networkinfrastructure component further comprises causing the prepared data tobe transmitted to a third network infrastructure component.
 9. Thesystem of claim 1, wherein the computer-executable instructions furthercause the system to: for each network infrastructure component of theplurality of network infrastructure components: electronically receivedata indicating one or more data standards; and electronically determinewhether the network infrastructure component is producing data based onthe received one or more data standards.
 10. The system of claim 1,wherein the computer-executable instructions further cause the system toperform: electronically receiving an indication that an event hasoccurred on the network; electronically predicting that data stored on afirst network infrastructure component of the plurality of networkinfrastructure components will be requested by a second networkinfrastructure component of the plurality of network infrastructurecomponents based on the indication that the event has occurred.
 11. Amethod in a network infrastructure component connected to a network, themethod comprising: transmitting data indicating data stored by thenetwork infrastructure component; receiving an indication that a portionof the data stored by the first network infrastructure component is tobe prepared to be transmitted to another network infrastructurecomponent based on a prediction that the portion of the data will berequired by the other network infrastructure component; preparing theportion of the data to be transmitted to the other networkinfrastructure component; receiving an indication that the other networkinfrastructure component requires the portion of the data; andtransmitting the portion of the data to the other network infrastructurecomponent.
 12. The method of claim 11, wherein transmitting the portionof the data comprises transmitting the portion of the data to the othernetwork infrastructure component in real-time.
 13. The method of claim12, wherein transmitting the portion of the data comprises transmittingthe portion of the data to the other network infrastructure component byusing a message broker.
 14. The method of claim 12, wherein transmittingthe portion of the data comprises streaming the portion of the data. 15.The method of claim 11, wherein the portion of the data comprises amodel used by the other network infrastructure component to process dataaccessible to the other network infrastructure component.
 16. The methodof claim 15, further comprising: receiving an indication that the modelwas altered by the other network infrastructure component; and alteringthe model based on the received indication that the model was altered bythe other network infrastructure component.
 17. The method of claim 11,wherein preparing the portion of the data further comprises: receivingan indication of an intermediary network infrastructure component; andtransmitting the data to the intermediary network infrastructurecomponent.
 18. One or more storage devices collectively storing anetwork infrastructure data structure for access and processing by aprogram executed by at least one computer processor that, when accessedand processed by at least one computer processor, functionally enablesthe at least one computer processor to: predict that a portion of datastored on a first network infrastructure component of a plurality ofnetwork infrastructure components will be requested by a second networkinfrastructure component of the plurality of network infrastructurecomponents; and cause the portion of data to be transmitted from thefirst network infrastructure component to the second networkinfrastructure component, the network infrastructure data structurecomprising: information specifying the plurality of networkinfrastructure components; for each network infrastructure component ofthe network infrastructure components: information indicating datastored by the network infrastructure component; and informationspecifying an indication that an event has occurred in the network, suchthat the information specifying an indication that an event has occurredin the network is usable to predict that the second networkinfrastructure component will request the portion of data, and such thatthe prediction that the second network infrastructure component willrequest the portion of data is usable to prepare the portion of data tobe transmitted from the first network infrastructure component to thesecond network infrastructure component.
 19. The one or more storagedevices of claim 18, wherein the portion of data further comprises amodel.
 20. The one or more storage devices of claim 18, wherein thenetwork infrastructure data structure further comprises: informationspecifying a third network infrastructure component of the plurality ofnetwork infrastructure components, such that preparing the data to betransmitted to the second network infrastructure component includestransmitting the data to the third network infrastructure component.